Decoding and Selective Modulation of Human Memory During Awake/Sleep Cycles

清醒/睡眠周期期间人类记忆的解码和选择性调制

• 批准号: 10289993
• 负责人: ITZHAK FRIED
• 金额: $ 147.85万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10289993
• 关键词: Acoustic Stimulation; Alzheimer' s Disease; Animals; Area; Artificial Intelligence; Auditory; Behavior; Brain; Brain region; Cells; Clinical; Code; Cognitive; Coupling; Data; Development; Electric Stimulation; Electrodes; Electrophysiology (science); Episodic memory; Event; Face; Foundations; Future; General Population; Goals; Hippocampus (Brain); Human; Intervention; Knowledge; Life; Light; Measures; Medial; Memory; Memory Disorders; Memory impairment; Methodology; Modeling; Nature; Neurodegenerative Disorders; Neurons; Neurosciences; Participant; Patient Monitoring; Pattern; Performance; Personal Satisfaction; Physiologic pulse; Physiological; Physiology; Play; Prevalence; Probability; Process; Protocols documentation; Research; Resolution; Response to stimulus physiology; Retrieval; Rodent; Sample Size; Sampling; Scheme; Series; Site; Sleep; Sleep Architecture; Slow-Wave Sleep; Stimulus; System; Techniques; Technology; Temporal Lobe; Testing; Therapeutic; Therapeutic Intervention; Time; Tsunami; Visual; Wakefulness; aging population; awake; deep learning; deep learning algorithm; experience; improved; innovation; interdisciplinary approach; interdisciplinary collaboration; learning strategy; memory consolidation; memory process; memory recognition; memory retrieval; movie; multidimensional data; multidisciplinary; multimodality; nervous system disorder; neural patterning; neuromechanism; novel; novel therapeutics; predictive modeling; recruit; relating to nervous system; sleep behavior; tool; white matter

PROJECT SUMMARY/ABSTRACT Episodic memories integrate the content of human experience in space and time and constitute the core of one's identity. Memory formation involves processing, and constructing interpretations of the incoming information in our daily lives and is one of the first functions compromised in neurodegenerative diseases such as Alzheimer's Disease. With population aging, we face a “Cognitive Tsunami” of millions of people with memory disorders. Thus, understanding neural mechanisms of memory, and finding interventions that enhance these processes is a critical endeavor with the potential to improve the lives of countless people world-wide. Although it is established that memory is critical for cognitive well-being, and sleep is critical for memory consolidation, the underlying mechanisms in the human brain are poorly understood. Research on memory and sleep so far has suffered from a gap between non-invasive cognitive research in humans and detailed electrophysiological research in animals. Moreover, most human studies are dominated by stimulus response methodologies where the presented stimuli constitute limited, discretized, aspects of memory. This approach, albeit well-controlled, is far from the rich narrative of episodes we experience. Thus, to mechanistically probe human memory consolidation, it is imperative to (a) employ methodologies that incorporate the continuous and multimodal nature of experience; (b) identify relevant neural activation patterns and how they are transformed and reactivated during sleep; (c) establish means to causally modulate memory processes during sleep. Building upon our exploratory U01 project, this proposal seeks a breakthrough in our understanding by going beyond the state-of-the-art, and via the application of integrative and multidisciplinary approaches. It capitalizes on a highly unique opportunity to record and modulate neuronal activity of a large number of single neurons and neuronal assemblies in the human brain. In parallel, it exploits the high dimensionality of the data as an asset through the use of cutting-edge Deep Learning (DL) algorithms, which have emerged as promising analysis tools. Specifically, the project will investigate the presence, and decoding, of distributed neural patterns associated with memory for different aspects of experience during wakefulness and identify their reactivation during sleep. The proposal aims to selectively modulate memory via application of novel closed-loop stimulation in sleep in concert with the DL model predictions. We anticipate that this project is poised to shed light on the relationship between sleep and memory, and its modulation from a novel perspective. Such an ambitious goal can only be achieved with unrivaled combination of experience, access to a clinical setting, and interdisciplinary collaborations such as those proposed in this project. By combining the opportunity to stimulate and record neural activity with the computational power of artificial intelligence, this project aims to offer findings with far reaching implications for basic neuroscience and contribute to the development of novel therapies for human memory disorders.

项目总结/摘要 情景记忆整合了人类在空间和时间上的经验内容，构成了一个人记忆的核心。 身份记忆的形成包括对输入信息的处理和解释， 它是我们日常生活中最先受到损害的神经退行性疾病之一， 疾病随着人口老龄化，我们面临着数百万记忆障碍患者的“认知海啸”。 因此，了解记忆的神经机制，并找到增强这些过程的干预措施， 这是一项重要的奋进，有可能改善全世界无数人的生活。 虽然记忆力对认知健康至关重要，睡眠对记忆力至关重要， 巩固，人类大脑中的潜在机制知之甚少。关于记忆和 到目前为止，睡眠一直受到人类非侵入性认知研究和详细研究之间的差距的影响。 动物的电生理学研究。此外，大多数人类研究都以刺激反应为主 方法，其中呈现的刺激构成有限的，离散的，记忆的方面。这种方法， 尽管控制得很好，但与我们所经历的情节的丰富叙述相去甚远。因此，为了机械地探测 人的记忆巩固，这是必要的（a）采用的方法，包括连续的， 经验的多模态性质;（B）识别相关的神经激活模式以及它们如何被转化 并在睡眠期间重新激活;（c）建立在睡眠期间因果调节记忆过程的方法。 在我们探索性的U 01项目的基础上，该提案寻求突破我们的理解， 超越最先进的水平，并通过应用综合和多学科的方法。它 利用一个非常独特的机会来记录和调节大量的神经元活动， 人脑中的单个神经元和神经元集合体。与此同时，它利用了高维 通过使用尖端的深度学习（DL）算法，将数据作为资产， 作为有前途的分析工具。具体来说，该项目将调查分布式的存在和解码， 在清醒和识别过程中，与记忆不同方面的经验相关的神经模式 它们在睡眠中重新激活。该提案旨在通过应用小说来选择性地调节记忆。 睡眠中的闭环刺激与DL模型预测一致。 我们预计该项目将揭示睡眠与记忆之间的关系，并且 从一个新的角度进行调整。只有通过无与伦比的组合才能实现这样一个雄心勃勃的目标 经验，获得临床环境，以及跨学科合作，如本报告中提出的 项目通过将刺激和记录神经活动的机会与计算能力相结合， 人工智能，该项目旨在提供对基础神经科学具有深远影响的发现， 有助于开发人类记忆障碍的新疗法。